A complex interplay between species governs the evolution of spatial patterns in ecology. An open problem in the biological sciences is characterizing spatio-temporal data and understanding how changes at the local scale affect global dynamics/behaviour. Here, we extend a well-studied temporal mathematical model of coral reef dynamics to include stochastic and spatial interactions and generate data to study different ecological scenarios. We present descriptors to characterize patterns in heterogeneous spatio-temporal data surpassing spatially averaged measures. We apply these descriptors to simulated coral data and demonstrate the utility of two topological data analysis techniques-persistent homology and zigzag persistence-for characterizing mechanisms of reef resilience. We show that the introduction of local competition between species leads to the appearance of coral clusters in the reef. We use our analyses to distinguish temporal dynamics stemming from different initial configurations of coral, showing that the neighbourhood composition of coral sites determines their long-term survival. Using zigzag persistence, we determine which spatial configurations protect coral from extinction in different environments. Finally, we apply this toolkit of multi-scale methods to empirical coral reef data, which distinguish spatio-temporal reef dynamics in different locations, and demonstrate the applicability to a range of datasets.

Plant-soil negative feedback (NF) is a well-established phenomenon that, by preventing the dominance of a single species, allows species coexistence and promotes the maintenance of biodiversity. At community scale, localized NF may cause the formation of exclusion zones under adult conspecifics leading to Janzen-Connell (JC) distribution. In this study, we explore the connection between adult density, either conspecifics or heterospecifics, on the probability of occurrence of JC distributions. Using an individual-based modelling approach, we simulated the formation of exclusion zones due to the build-up of NF in proximity of conspecific adult plants and assessed the frequency of JC distribution in relation to conspecifics and heterospecifics density ranging from isolated trees to closed forest stands. We found that JC recruitment distribution is very common in the case of an isolated tree when NF was strong and capable to form an exclusion zone under the parent tree. At very low NF intensity, a prevalence of the decreasing pattern was observed because, under such conditions, the inhibitory effect due to the presence of the mother tree was unable to overcome the clustering effect of the seed dispersal kernel. However, if NF is strong the JC frequency suddenly decreases in stands with a continuous conspecific cover likely as a result of progressive expansion of the exclusion zone surrounding all trees in closed forest stands. Finally, our simulations showed that JC distribution should not be frequent in the case of rare species immersed in a matrix of heterospecific adults. Overall, the model shows that a plant suffering from strong NF in monospecific stands can rarely exhibit a recruitment pattern fitting the JC model. Such counterintuitive results would provide the means to reconcile the well-established NF framework with part the forest ecologists\' community that is still skeptical towards the JC model.
UNASSIGNED: Our model highlights the complex interconnection between NF intensity, stand density, and recruitment patterns explaining where and why the JC distribution occurs. Moreover, predicting the occurrence of JC in relation to stand density we clarify the relevance of this ecological phenomenon for future integration in plant community frameworks.

Nonpharmaceutical interventions (NPI) and ring vaccination (i.e., vaccination that primarily targets contacts and contacts of contacts of Ebola cases) are currently used to reduce the spread of Ebola during outbreaks. Because these measures are typically initiated after an outbreak is declared, they are limited by real-time implementation challenges. Preventive vaccination may provide a complementary option to help protect communities against unpredictable outbreaks. This study aimed to assess the impact of preventive vaccination strategies when implemented in conjunction with NPI and ring vaccination.
A spatial-explicit, individual-based model (IBM) that accounts for heterogeneity of human contact, human movement, and timing of interventions was built to represent Ebola transmission in the Democratic Republic of the Congo. Simulated preventive vaccination strategies targeted healthcare workers (HCW), frontline workers (FW), and the general population (GP) with varying levels of coverage (lower coverage: 30% of HCW/FW, 5% of GP; higher coverage: 60% of HCW/FW, 10% of GP) and efficacy (lower efficacy: 60%; higher efficacy: 90%).
The IBM estimated that the addition of preventive vaccination for HCW reduced cases, hospitalizations, and deaths by ∼11 % to ∼25 % compared with NPI + ring vaccination alone. Including HCW and FW in the preventive vaccination campaign yielded ∼14 % to ∼38 % improvements in epidemic outcomes. Further including the GP yielded the greatest improvements, with ∼21 % to ∼52 % reductions in epidemic outcomes compared with NPI + ring vaccination alone. In a scenario without ring vaccination, preventive vaccination reduced cases, hospitalizations, and deaths by ∼28 % to ∼59 % compared with NPI alone. In all scenarios, preventive vaccination reduced Ebola transmission particularly during the initial phases of the epidemic, resulting in flatter epidemic curves.
The IBM showed that preventive vaccination may reduce Ebola cases, hospitalizations, and deaths, thus safeguarding the healthcare system and providing more time to implement additional interventions during an outbreak.

The North Sea and the Baltic Sea, including Danish coastal waters, have experienced a drastic decline in eelgrass Zostera marina coverage during the past century. Around 1900, eelgrass meadows covered about 6700 km2 of Danish coastal waters while the current potential distribution area is only about one third of this. In some areas, the potential distribution area is far from realized, and restoration efforts are needed to assist recovery. Such efforts are challenging, and resource-demanding and careful site selection is, therefore, important. In the present study, we aim to identify the connectivity of eelgrass populations as a basis for guiding site selection for restoration. We developed a coupled biophysical model to study eelgrass dispersal in the Kattegat. Partly submerged particles simulated the dispersal of reproductive eelgrass shoots containing seeds during the flowering season July-September. We then used network analysis to identify the potential connectivity between populations. We evaluated connectivity based on In-strength, Betweenness and Eigenvector centrality metrics and identified key areas in the Kattegat such as the central part of Aalborg Bay, to be considered to restore the network of Z. marina patches. The study proves the potentials of combining hydrodynamic models and network analysis to support marine conservation and planning, and highlights the importance of collaboration between ecologists, oceanographers, and practitioners in this endeavour.

Bioenergetic models describe the processes through which animals acquire energy from resources in the environment and allocate it to different life history functions. They capture some of the fundamental mechanisms regulating individuals, populations and ecosystems and have thus been used in a wide variety of theoretical and applied contexts. Here, I review the development of bioenergetic models for marine mammals and their application to management and conservation. For these long-lived, wide-ranging species, bioenergetic approaches were initially used to assess the energy requirements and prey consumption of individuals and populations. Increasingly, models are developed to describe the dynamics of energy intake and allocation and predict how resulting body reserves, vital rates and population dynamics might change as external conditions vary. The building blocks required to develop such models include estimates of intake rate, maintenance costs, growth patterns, energy storage and the dynamics of gestation and lactation, as well as rules for prioritizing allocation. I describe how these components have been parameterized for marine mammals and highlight critical research gaps. Large variation exists among available analytical approaches, reflecting the large range of life histories, management needs and data availability across studies. Flexibility in modelling strategy has supported tailored applications to specific case studies but has resulted in limited generality. Despite the many empirical and theoretical uncertainties that remain, bioenergetic models can be used to predict individual and population responses to environmental change and other anthropogenic impacts, thus providing powerful tools to inform effective management and conservation.

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Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs.

HIV viral load (VL) is an important predictor of HIV progression and transmission. Anti-retroviral therapy (ART) has been reported to reduce HIV transmission by lowering VL. However, apart from this beneficial effect, increased levels of population mean set-point viral load (SPVL), an estimator for HIV virulence, have been observed in men who have sex with men (MSM) in the decade following the introduction of ART in The Netherlands. Several studies have been devoted to explain these counter-intuitive trends in SPVL. However, to our knowledge, none of these studies has investigated an explanation in which it arises as the result of a sexually transmitted infection (STI) co-factor in detail. In this study, we adapted an event-based, individual-based model to investigate how STI co-infection and sexual risk behaviour affect the evolution of HIV SPVL in MSM before and after the introduction of ART. The results suggest that sexual risk behaviour has an effect on SPVL and indicate that more data are needed to test the effect of STI co-factors on SPVL. Furthermore, the observed trends in SPVL cannot be explained by sexual risk behaviour and STI co-factors only. We recommend to develop mathematical models including also factors related to viral evolution as reported earlier in the literature. However, this requires more complex models, and the collection of more data for parameter estimation than what is currently available.

Harmonious coexistence between humans, other animals and ecosystem services they support is a complex issue, typically impacted by landscape change, which affects animal distribution and abundance. In the last 30 years, afforestation on grasslands across Great Britain has been increasing, motivated by socio-economic reasons and climate change mitigation. Beyond expected benefits, an obvious question is what are the consequences for wider biodiversity of this scale of landscape change. Here, we explore the impact of such change on the expanding population of common buzzards Buteo buteo, a raptor with a history of human-induced setbacks. Using Resource-Area-Dependence Analysis (RADA), with which we estimated individuals\' resource needs using 10-day radio-tracking sessions and the 1990s Land Cover Map of GB, and agent-based modelling, we predict that buzzards in our study area in lowland UK had fully recovered (to 2.2 ind km-2) by 1995. We also anticipate that the conversion of 30%, 60% and 90% of economically viable meadow into woodland would reduce buzzard abundance nonlinearly by 15%, 38% and 74%, respectively. The same approach used here could allow for cost-effective anticipation of other animals\' population patterns in changing landscapes, thus helping to harmonize economy, landscape change and biodiversity.

The patterns of collective behaviour in a population emerging from individual animal movement have long been of interest to ecologists, as has the emergence of heterogeneous patterns among a population. In this paper we will consider these phenomena by using an individual-based modelling approach to simulate a population whose individuals undergo density-dependent movement in 2D spatial domains. We first show that the introduction of density-dependent movement in the form of two parameters, a perception radius and a probability of directed movement, leads to the formation of clusters. We then show that the properties of the clusters and their stability over time are different between populations of Brownian and non-Brownian walkers and are also dependent on the choice of parameters. Finally, we consider the effect of the probability of directed movement on the temporal stability of clusters and show that while clusters formed by Brownian and non-Brownian walkers may have similar properties with certain parameter sets, the spatio-temporal dynamics remain different.